{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import cifar10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images,train_labels),(test_images,test_labels) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 32, 32, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x14f8c5ef0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(3,3))\n",
    "plt.imshow(train_images[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
